cvxportfolio
is a python library for portfolio optimization and simulation
based on the book Multi-Period Trading via Convex Optimization
(also available in print).
The documentation of the package is kindly hosted by Read the Docs at www.cvxportfolio.com. We also show some of our tutorials and examples on our youtube channel.
All our source code and releases are kindly hosted by the Python Package Index. You can install the latest one with
pip install -U cvxportfolio
You can see how this works on our Installation and Hello World youtube video.
We ship our unit test suite with the pip package. After installing you can test in you local environment by
python -m unittest discover cvxportfolio
In the following example market data is downloaded by a public source
(Yahoo finance) and the forecasts are computed iteratively, at each point in the backtest, from past data.
That is, at each point in the backtest,
the policy object only operates on past data, and thus the result you get is a realistic simulation of what the strategy would have performed in the market.
The simulator by default includes holding and transaction costs, using the models described in the book, and default parameters that are typical for the US stock market.
The logic used
matches what is described in Chapter 7 of the book. For example, returns are forecasted as the historical mean returns
and covariances as historical covariances (both ignoring np.nan
's). The logic used is detailed in the forecast
module. Many optimizations
are applied to make sure the system works well with real data.
import cvxportfolio as cvx
gamma = 3 # risk aversion parameter (Chapter 4.2)
kappa = 0.05 # covariance forecast error risk parameter (Chapter 4.3)
objective = cvx.ReturnsForecast() - gamma * (
cvx.FullCovariance() + kappa * cvx.RiskForecastError()
) - cvx.StocksTransactionCost()
constraints = [cvx.LeverageLimit(3)]
policy = cvx.MultiPeriodOptimization(objective, constraints, planning_horizon=2)
simulator = cvx.StockMarketSimulator(['AAPL', 'AMZN', 'TSLA', 'GM', 'CVX', 'NKE'])
result = simulator.backtest(policy, start_time='2020-01-01')
# print backtest result statistics
print(result)
# plot backtest results
result.plot()
We show in the example on user-provided forecasters how the user can define custom classes to forecast the expected returns and covariances. These provide callbacks that are executed at each point in time during the backtest. The system enforces causality and safety against numerical errors. We recommend to always include the default forecasters that we provide in any analysis you may do, since they are very robust and well-tested.
We show in the examples on DOW30 components and wide assets-classes ETFs how a simple sweep over hyper-parameters, taking advantage of our sophisticated parallel backtest machinery, quickly provides results on the best strategy to apply to any given selection of assets.
To set up a development environment locally you should
git clone https://github.com/cvxgrp/cvxportfolio.git
cd cvxportfolio
make env
This will replicate our development environment. From there you can test with
make test
You activate the shell environment with one of scripts in env/bin
(or env\Scripts
on windows), for example if you use bash on POSIX
source env/bin/activate
and from the environment you can run any of the scripts in the examples (the cvxportfolio package is installed in editable mode).
Or, if you don't want to activate the environment, you can just run scripts directly using env/bin/python
or env\Scripts\python
on windows, like we do in the Makefile.
In branch 0.0.X you can find the original material used to generate plots and results in the book. As you may see from those ipython notebooks a lot of the logic that was implemented there, outside of cvxportfolio proper, is being included and made automatic in newer versions of cvxportfolio.
If you use cvxportfolio
in your academic work please cite our book:
@book{BBDKKNS:17,
author = {S. Boyd and E. Busseti and S. Diamond and R. Kahn and K. Koh and P. Nystrup and J. Speth},
title = {Multi-Period Trading via Convex Optimization},
series = {Foundations and Trends in Optimization},
year = {2017},
month = {August},
publisher = {Now Publishers},
url = {http://stanford.edu/~boyd/papers/cvx_portfolio.html},
}
Cvxportfolio is licensed under the Apache 2.0 permissive open source license.